A novel bias-alleviated hybrid ensemble model based on over-sampling and post-processing for fair classification

References

• Agrawal, D. K., Kirar, B. S., & Pachori, R. B. (2019). Automated glaucoma detection using quasi-bivariate variational mode decomposition from fundus images. IET Image Processing, 13(13), 2401–2408. https://doi.org/10.1049/iet-ipr.2019.0036
   Web of Science ®Google Scholar
• Asuncion, A., & Newman, D. (2007). UCI machine learning repository. School of Information and Computer Science, University of California. http://www.ics.uci.edu/~mlearn/MLRepository.html
   Google Scholar
• Bellamy, R. K. E., Mojsilovic, A., Nagar, S., Ramamurthy, K. N., Richards, J., Saha, D., Sattigeri, P., Singh, M., Varshney, K. R., Zhang, Y., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., & Mehta, S. (2019). AI fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM Journal of Research and Development, 63(4/5), 4:1-4:15. https://doi.org/10.1147/JRD.2019.2942287
   Web of Science ®Google Scholar
• Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/BF00058655
   Web of Science ®Google Scholar
• Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010). The balanced accuracy and its posterior distribution. In Proceedings of the 20th International Conference on Pattern Recognition, August 23–26, Istanbul, Turkey, pp. 3121–3124.
   Google Scholar
• Calmon, F., Wei, D., Vinzamuri, B., Ramamurthy, K. N., & Varshney, K. R. (2017). Optimized pre-processing for discrimination prevention. Advances in Neural Information Processing Systems, 30, 3992–4001. https://proceedings.neurips.cc/paper/2017/hash/9a49a25d845a483fae4be7e341368e36-Abstract.html
   Google Scholar
• Celis, L. E., Huang, L., Keswani, V., & Vishnoi, N. K. (2019). Classification with fairness constraints: A meta-algorithm with provable guarantees. In Proceedings of 2019 ACM Conference on Fairness, Accountability, and Transparency, January 29–31, Atlanta, GA, USA, pp. 319–328.
   Google Scholar
• Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
   Web of Science ®Google Scholar
• Chen, T. Q., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 13–17, San Francisco, USA, pp. 785–794.
   Google Scholar
• d’Alessandro, B., O’Neil, C., & LaGatta, T. (2017). Conscientious classification: A data scientist’s guide to discrimination-aware classification. Big Data, 5(2), 120–134. https://doi.org/10.1089/big.2016.0048
   PubMed Web of Science ®Google Scholar
• Dastile, X., Celik, T., & Potsane, M. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing, 91, 106263. https://doi.org/10.1016/j.asoc.2020.106263
   Web of Science ®Google Scholar
• Devi, D., Biswas, S. K., & Purkayastha, B. (2019). Learning in presence of class imbalance and class overlapping by using one-class SVM and undersampling technique. Connection Science, 31(2), 105–142. https://doi.org/10.1080/09540091.2018.1560394
   Web of Science ®Google Scholar
• Fish, B., Kun, J., & Lelkes, Á. D. (2016). A confidence-based approach for balancing fairness and accuracy. In Proceedings of 2016 SIAM International Conference on Data Mining, June 5–7, Miami, Florida, USA, pp. 144–152.
   Google Scholar
• Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proceedings of the 13th International Conference on Machine Learning, July 3–6, Bari, Italy, pp. 148–156.
   Google Scholar
• Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
   Web of Science ®Google Scholar
• Guzmán-Ponce, A., Sánchez, J. S., Valdovinos, R. M., & Marcial-Romero, J. R. (2021). DBIG-US: A two-stage under-sampling algorithm to face the class imbalance problem. Expert Systems with Applications, 168, 114301. https://doi.org/10.1016/j.eswa.2020.114301
   Web of Science ®Google Scholar
• Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 29, 3315–3323. https://proceedings.neurips.cc/paper/2016/hash/9d2682367c3935defcb1f9e247a97c0d-Abstract.html
   Google Scholar
• He, H. B., Bai, Y., Garcia, E. A., & Li, S. T. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In Proceedings of 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), June 1–8, Hong Kong, China, pp. 1322–1328.
   Google Scholar
• Iosifidis, V., Fetahu, B., & Ntoutsi, E. (2019). FAE: A fairness-aware ensemble framework. In Proceedings of 2019 IEEE International Conference on Big Data, December 9–12, Los Angeles, CA, USA, pp. 1375–1380.
   Google Scholar
• Iosifidis, V., & Ntoutsi, E. (2019). Adafair: Cumulative fairness adaptive boosting. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, November 3–7, Beijing, China, pp. 781–790.
   Google Scholar
• Jia, M. L., Reiter, A., Lim, S. N., Artzi, Y., & Cardie, C. (2021). When in doubt: improving classification performance with alternating normalization. arXiv preprint arXiv:2109.13449.
   Google Scholar
• Jiang, M. X., Yang, Y. L., & Qiu, H. Q. (2022). Fuzzy entropy and fuzzy support-based boosting random forests for imbalanced data. Applied Intelligence, 52(4), 4126–4143. https://doi.org/10.1007/s10489-021-02620-y
   Web of Science ®Google Scholar
• Kamiran, F., & Calders, T. (2009). Classifying without discriminating. In Proceedings of 2009 2nd International Conference on Computer, Control and Communication, February 17–18, Karachi, Pakistan, pp. 1–6.
   Google Scholar
• Kamiran, F., & Calders, T. (2012). Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems, 33(1), 1–33. https://doi.org/10.1007/s10115-011-0463-8
   Web of Science ®Google Scholar
• Ke, G. L., Meng, Q., Finley, T., Wang, T. F., Chen, W., Ma, W. D., Ye, Q. W., & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In Proceedings of Annual 2017 Conference on Neural Information Processing Systems, December 4–9, California, USA, pp. 3146–3154.
   Google Scholar
• Kearns, M., Neel, S., Roth, A., & Wu, Z. S. (2019). An empirical study of rich subgroup fairness for machine learning. In Proceedings of 2019 ACM Conference on Fairness, Accountability, and Transparency, January 29–31, Atlanta, GA, USA, pp. 100–109.
   Google Scholar
• Kim, S., Joshi, P., Kalsi, P. S., & Taheri, P. (2018). Crime analysis through machine learning. In Proceedings of 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), November 1–3, University of British Columbia, Vancouver, Canada, pp. 415–420.
   Google Scholar
• Kirar, B. S., & Agrawal, D. K. (2019). Computer aided diagnosis of glaucoma using discrete and empirical wavelet transform from fundus images. IET Image Processing, 13(1), 73–82. https://doi.org/10.1049/iet-ipr.2018.5297
   Web of Science ®Google Scholar
• Kirar, B. S., Agrawal, D. K., & Kirar, S. (2022). Glaucoma detection using image channels and discrete wavelet transform. IETE Journal of Research, 68(6), 4421–4428. https://doi.org/10.1080/03772063.2020.1795934
   Web of Science ®Google Scholar
• Kohavi, R. (1996). Scaling up the accuracy of naive-Bayes classifiers: A decision-tree hybrid. In Proceedings of 1996 International Conference on Knowledge Discovery and Data Mining, August 2–4, Oregon, USA, pp. 202–207.
   Google Scholar
• Li, X., & Li, K. W. (2022). Imbalanced data classification based on improved EIWAPSO-AdaBoost-C ensemble algorithm. Applied Intelligence, 52(6), 6477–6502. https://doi.org/10.1007/s10489-021-02708-5
   Web of Science ®Google Scholar
• Liu, W. H., Hu, E. W., Su, B. G., & Wang, J. (2021). Using machine learning techniques for DSP software performance prediction at source code level. Connection Science, 33(1), 26–41. https://doi.org/10.1080/09540091.2020.1762542
   Web of Science ®Google Scholar
• Liu, X. Y., Wu, J., & Zhou, Z. H. (2008). Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39(2), 539–550. https://doi.org/10.1109/TSMCB.2008.2007853
   PubMed Web of Science ®Google Scholar
• Lohia, P. K., Ramamurthy, K. N., Bhide, M., Saha, D., Varshney, K. R., & Puri, R. (2019). Bias mitigation post-processing for individual and group fairness. In Proceedings of 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, May 12–17, Brighton, UK, pp. 2847–2851.
   Google Scholar
• Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6), 1–35. https://doi.org/10.1145/3457607
   Web of Science ®Google Scholar
• Moro, S., Cortez, P., & Rita, P. (2014). A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62, 22–31. https://doi.org/10.1016/j.dss.2014.03.001
   Web of Science ®Google Scholar
• Onan, A. (2018). An ensemble scheme based on language function analysis and feature engineering for text genre classification. Journal of Information Science, 44(1), 28–47. https://doi.org/10.1177/0165551516677911
   Web of Science ®Google Scholar
• Onan, A. (2019). Consensus clustering-based undersampling approach to imbalanced learning. Scientific Programming, https://doi.org/10.1155/2019/5901087
   Web of Science ®Google Scholar
• Onan, A., Korukoğlu, S., & Bulut, H. (2016). Ensemble of keyword extraction methods and classifiers in text classification. Expert Systems with Applications, 57, 232–247. https://doi.org/10.1016/j.eswa.2016.03.045
   Web of Science ®Google Scholar
• Onan, A., Korukoğlu, S., & Bulut, H. (2017). A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification. Information Processing & Management, 53(4), 814–833. https://doi.org/10.1016/j.ipm.2017.02.008
   Web of Science ®Google Scholar
• Onan, A., & Toçoğlu, M. A. (2021). A term weighted neural language model and stacked bidirectional LSTM based framework for sarcasm identification. IEEE Access, 9, 7701–7722. https://doi.org/10.1109/ACCESS.2021.3049734
   Web of Science ®Google Scholar
• Petrović, A., Nikolić, M., Radovanović, S., Delibašić, B., & Jovanović, M. (2022). FAIR: Fair adversarial instance re-weighting. Neurocomputing, 476, 14–37. https://doi.org/10.1016/j.neucom.2021.12.082
   Web of Science ®Google Scholar
• Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., & Weinberger, K. Q. (2017). On fairness and calibration. In Proceedings of 31st Conference on Neural Information Processing Systems, December 4–9, Long Beach, CA, USA, pp. 5684–5693.
   Google Scholar
• Potha, N., Kouliaridis, V., & Kambourakis, G. (2021). An extrinsic random-based ensemble approach for android malware detection. Connection Science, 33(4), 1077–1093. https://doi.org/10.1080/09540091.2020.1853056
   Web of Science ®Google Scholar
• Puntumapon, K., Rakthamamon, T., & Waiyamai, K. (2016). Cluster-based minority over-sampling for imbalanced datasets. IEICE Transactions on Information and Systems, 99(12), 3101–3109. https://doi.org/10.1587/transinf.2016EDP7130
   Google Scholar
• Qi, W., Ovur, S. E., Li, Z. J., Marzullo, A., & Song, R. (2021). Multi-sensor guided hand gesture recognition for a teleoperated robot using a recurrent neural network. IEEE Robotics and Automation Letters, 6(3), 6039–6045. https://doi.org/10.1109/LRA.2021.3089999
   Web of Science ®Google Scholar
• Rezaei, A., Fathony, R., Memarrast, O., & Ziebart, B. (2020). Fairness for robust log loss classification. In Proceedings of the 2020 AAAI Conference on Artificial Intelligence, February 7–12, New York, USA, pp. 5511–5518.
   Google Scholar
• Stehman, S. V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62(1), 77–89. https://doi.org/10.1016/S0034-4257(97)00083-7
   Web of Science ®Google Scholar
• Tao, X. M., Li, Q., Ren, C., Guo, W. J., Li, C. X., He, Q., Liu, R., & Zou, J. R. (2019). Real-value negative selection over-sampling for imbalanced data set learning. Expert Systems with Applications, 129, 118–134. https://doi.org/10.1016/j.eswa.2019.04.011
   Web of Science ®Google Scholar
• Thabtah, F., Hammoud, S., Kamalov, F., & Gonsalves, A. (2020). Data imbalance in classification: Experimental evaluation. Information Sciences, 513, 429–441. https://doi.org/10.1016/j.ins.2019.11.004
   Web of Science ®Google Scholar
• Wang, J. L., Liu, Y., & Levy, C. (2021). Fair classification with group-dependent label noise. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, March 3–10, New York, USA, pp. 526–536.
   Google Scholar
• Wang, Y. L., Zhang, Y. H., Lu, Y., & Yu, X. R. (2020). A comparative assessment of credit risk model based on machine learning — a case study of bank loan data. Procedia Computer Science, 174, 141–149. https://doi.org/10.1016/j.procs.2020.06.069
   Google Scholar
• Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259. https://doi.org/10.1016/S0893-6080(05)80023-1
   Web of Science ®Google Scholar
• Xia, Y. F., Liu, C. Z., Da, B., & Xie, F. M. (2018). A novel heterogeneous ensemble credit scoring model based on bstacking approach. Expert Systems with Applications, 93, 182–199. https://doi.org/10.1016/j.eswa.2017.10.022
   Web of Science ®Google Scholar
• Xia, Y. L., Chen, K., & Yang, Y. (2021). Multi-label classification with weighted classifier selection and stacked ensemble. Information Sciences, 557, 421–442. https://doi.org/10.1016/j.ins.2020.06.017
   Web of Science ®Google Scholar
• Xu, Y., Yang, C. J., Peng, S. L., & Nojima, Y. (2020). A hybrid two-stage financial stock forecasting algorithm based on clustering and ensemble learning. Applied Intelligence, 50(11), 3852–3867. https://doi.org/10.1007/s10489-020-01766-5
   Web of Science ®Google Scholar
• Zafar, M. B., Valera, I., Gomez Rodriguez, M., & Gummadi, K. P. (2017). Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th International Conference on World Wide Web Companion, April 3–7, Perth, Australia, pp. 1171–1180.
   Google Scholar
• Zhang, W. Y., Yang, D. Q., & Zhang, S. (2021). A new hybrid ensemble model with voting-based outlier detection and balanced sampling for credit scoring. Expert Systems with Applications, 174, 114744. https://doi.org/10.1016/j.eswa.2021.114744
   Web of Science ®Google Scholar